NOT FOR DISSEMINATION
Recoded for any value > $250,000
The method for classifying Cleveland’s neighborhoods into a typology is a two step process. First, neighborhood features relevant for understanding the local housing market are entered into a principal component analysis (PCA). PCA works by taking several pieces of information, or variables, about a neighborhood that can be directly observed and measured (e.g., home sales, housing density, etc.), and using the underlying relationships between those variables to uncover new variables that cannot be directly observed or measured, but that may be important defining features of the neighborhood. The variables entered into the PCA were:
The PCA distilled the relationships between these nine variables into two new components:
Component 1: an index of distress that describes low home values, above average demolitions and foreclosures, and relatively low sales rates
Component 2: an index of density, defined by above average rates of rental housing and commercial property area
The components were then used as inputs into a cluster analysis. The clustering algorithm settled on a solution categorizing block groups into five neighborhood types/clusters.